14 citations · 40 across the 6 of their papers we have counts for
4 papers · 1 filter
Real-Time Active Learning for optimised spectroscopic follow-up: Enhancing early SN Ia classification with the Fink broker
A. Möller, E. E. O. Ishida, J. Peloton +6
Current and future surveys rely on machine learning classification to obtain large and complete samples of transients. Many of these algorithms are restricted by training samples t…
Real-bogus scores for active anomaly detection
T. A. Semenikhin, M. V. Kornilov, M. V. Pruzhinskaya +8
In the task of anomaly detection in modern time-domain photometric surveys, the primary goal is to identify astrophysically interesting, rare, and unusual objects among a large vol…
Transient Classifiers for Fink: Benchmarks for LSST
B. M. O. Fraga, C. R. Bom, A. Santos +6
The upcoming Legacy Survey of Space and Time (LSST) is expected to detect a few million transients per night, which will generate a live alert stream during the entire ten years of…
Enabling the discovery of fast transients: A kilonova science module for the Fink broker
B. Biswas, E. E. O. Ishida, J. Peloton +4
We describe the fast transient classification algorithm in the center of the kilonova (KN) science module currently implemented in the Fink broker and report classification results…